Pitchclass2vec: Symbolic Music Structure Segmentation with Chord Embeddings

Structure perception is a fundamental aspect of music cognition in humans. Historically, the hierarchical organization of music into structures served as a narrative device for conveying meaning, creating expectancy, and evoking emotions in the listener. Thereby, musical structures play an essential role in music composition, as they shape the musical discourse through which the composer organises his ideas. In this paper, we present a novel music segmentation method, pitchclass2vec, based on symbolic chord annotations, which are embedded into continuous vector representations using both natural language processing techniques and custom-made encodings. Our algorithm is based on long-short term memory (LSTM) neural network and outperforms the state-of-the-art techniques based on symbolic chord annotations in the field

Semantic Integration of MIR Datasets with the Polifonia Ontology Network

Integration between different data formats, and between data belonging to different collections, is an ongoing challenge in the MIR field. Semantic Web tools have proved to be promising resources for making different types of music information interoperable. However, the use of these technologies has so far been limited and scattered in the field. To address this, the Polifonia project is developing an ontological ecosystem that can cover a wide variety of musical aspects (musical features, instruments, emotions, performances). In this paper, we present the Polifonia Ontology Network, an ecosystem that enables and fosters the transition towards semantic MIR

PROPOSAL OF SCALE DIAGRAMMATIC FOR QUANTIFICATION OF CERCOSPORA OF BEET

Cercospora leaf. Spot (Cercospora beticola) is present in almost all fields where beet (Beta vulgaris) is cultivated and it can cause sugar yielding loss up to 40%. The aim of this work was to design and validate a diagrammatic scale for this disease. For the design of the diagrammatic scale, 95 sugar beet leaves were collected randomly in the metropolitan region of Curitiba, PR. The maximum and minimum proportions of damaged leaf area were considered and the intermediate levels of severity were determined according to "Weber and Fechner’s Stimulus Law". The proposed validation of the diagrammatic scale was conducted from 35 sugar beet leaves with different levels of severity, with and without the proposed diagrammatic scale. The use of the scale provided more accuracy and precision in the visual estimates, being indicated in epidemiological studies and may provide more accurate information in order to develop management strategies for the beet’s Cercospora leaf. SpotA cercosporiose (Cercospora beticola) esta presente praticamente em todos os lugares onde se cultiva beterraba (Beta vulgaris), podendo provocar perda de rendimento de açúcar de até 40%. O objetivo deste trabalho foi elaborar e validar uma escala diagramática para esta doença. Para elaboração da escala diagramática foram coletadas, aleatoriamente, 95 folhas de beterraba, na região Metropolitana de Curitiba, PR. Considerou-se a máxima e mínima proporção de área foliar lesionada e os níveis intermediários de severidade foram determinados seguindo a "Lei do estímulo de Weber e Fechner". A validação foi realizada a partir de 35 folhas de beterraba com diferentes níveis de severidade, sem e posteriormente com o auxílio da escala diagramática proposta. O uso da escala proporcionou maior acurácia e precisão das estimativas visuais, sendo indicada em estudos epidemiológicos e poderá proporcionar informações mais adequadas para estabelecer estratégias de manejo para a cercospora da beterraba

The HaMSE Ontology: Using Semantic Technologies to support Music Representation Interoperability and Musicological Analysis

The use of Semantic Technologies - in particular the Semantic Web - has revealed to be a great tool for describing the cultural heritage domain and artistic practices. However, the panorama of ontologies for musicological applications seems to be limited and restricted to specific applications. In this research, we propose HaMSE, an ontology capable of describing musical features that can assist musicological research. More specifically, HaMSE proposes to address sues that have been affecting musicological research for decades: the representation of music and the relationship between quantitative and qualitative data. To do this, HaMSE allows the alignment between different music representation systems and describes a set of musicological features that can allow the music analysis at different granularity levels

ChoCo: a Chord Corpus and a Data Transformation Workflow for Musical Harmony Knowledge Graphs

Abstract Various disconnected chord datasets are currently available for music analysis and information retrieval, but they are often limited by either their size, non-openness, lack of timed information, and interoperability. Together with the lack of overlapping repertoire coverage, this limits cross-corpus studies on harmony over time and across genres, and hampers research in computational music analysis (chord recognition, pattern mining, computational creativity), which needs access to large datasets. We contribute to address this gap, by releasing the Chord Corpus (ChoCo), a large-scale dataset that semantically integrates harmonic data from 18 different sources using heterogeneous representations and formats (Harte, Leadsheet, Roman numerals, ABC, etc.). We rely on JAMS (JSON Annotated Music Specification), a popular data structure for annotations in Music Information Retrieval, to represent and enrich chord-related information (chord, key, mode, etc.) in a uniform way. To achieve semantic integration, we design a novel ontology for modelling music annotations and the entities they involve (artists, scores, etc.), and we build a 30M-triple knowledge graph, including 4 K+ links to other datasets (MIDI-LD, LED)